1. Field of the Invention
The invention relates to monitoring of a person's sleep pattern.
2. Prior Art Discussion
It is known to provide a system to receive and process signals from sensors in order to monitor a person's sleep pattern. In one approach sleep stages are determined using signals from a polysomnogram system, in which the sleep staging component is based on measuring electroencephalograms (EEG) which are a direct measurement of brain activity. This approach has a number of disadvantages. First of all, polysomnogram monitoring equipment is complex and generally needs to be operated and analysed in a clinic by skilled technicians. The patient is required to visit a clinic for an overnight study where skilled technicians attach the electrodes to the head, chest, chin and leg, together with a chest band and an airflow monitor. This is a costly and time-consuming process. If the polysomnogram system is operated by a patient at home, there is the requirement that the electrodes are attached correctly, and in particular that the EEG electrodes are correctly placed and attach, or otherwise the extremely low voltage EEG signals will not be recorded correctly. Furthermore, the use of a number of electrodes attached to the head during sleep is uncomfortable and disrupts the patient's sleep.
In another approach, motion based systems (actimetry) are used. However, such systems have the disadvantage that they can only distinguish between sleep and wake, with poor accuracy in patients with sleep disorders.
U.S. Pat. No. 5,280,791 describes an approach in which cardiac R-R wave intervals are used to designate sleep as REM or non-REM. A power spectrum of the cardiac R-R interval is calculated.
The prior art systems do not appear to analyse specific sleep stages sufficiently to recognise periods of wakefulness. In addition, where stages such as REM and non-REM are differentiated it appears that the performance is quite poor as the decision is based on comparison of a single parameter with a previously determined threshold value.
Therefore the current state of the art in determining sleep stages is limited by (a) the need to directly measure brain activity, and (b) poor performance when using observations of single parameters of cardiac activity.